2 citations · 3 across the 6 of their papers we have counts for
6 papers
TAP into the Patch Tokens: Leveraging Vision Foundation Model Features for AI-Generated Image Detection
Ahmed Abdullah, Nikolas Ebert, Oliver Wasenmüller
Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, effectively detect AI-generated images (AIGIs) from unseen generative models when u…
PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms
Laurenz Reichardt, Nikolas Ebert, Oliver Wasenmüller
3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AMD, and embedded hardware. We…
Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural Networks for Image Classification
Michael Schulze, Nikolas Ebert, Laurenz Reichardt +1
This paper investigates novel classifier ensemble techniques for uncertainty calibration applied to various deep neural networks for image classification. We evaluate both accuracy…
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks
René Schuster, Oliver Wasenmüller, Christian Unger +1
Dense pixel matching is important for many computer vision tasks such as disparity and flow estimation. We present a robust, unified descriptor network that considers a large conte…
Automated Scene Flow Data Generation for Training and Verification
Oliver Wasenmüller, René Schuster, Didier Stricker +6
Scene flow describes the 3D position as well as the 3D motion of each pixel in an image. Such algorithms are the basis for many state-of-the-art autonomous or automated driving fun…
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning
Patrik Feth, Mohammed Naveed Akram, René Schuster +1
Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driv…